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Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials
Author(s) -
Chambaz Antoine,
Laan Mark J.
Publication year - 2014
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12013
Subject(s) - covariate , mathematics , inference , statistical inference , sequential analysis , statistics , independent and identically distributed random variables , sampling (signal processing) , group (periodic table) , econometrics , computer science , random variable , artificial intelligence , chemistry , organic chemistry , filter (signal processing) , computer vision
This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.